\section{计算机视觉}
这一章是计算机视觉部分，主要侧重在底层特征提取，视频分析，跟踪，目标检测和识别方面等方面。对于自己不太熟悉的领域比如摄像机标定和立体视觉，仅仅列出上google上引用次数比较多的文献。有一些刚刚出版的文章，个人非常喜欢，也列出来了。

\subsection{Active Appearance Models}
活动表观模型和活动轮廓模型基本思想来源Snake，现在在人脸三维建模方面得到了很成功的应用，这里列出了三篇最早最经典的文章。对这个领域有兴趣的可以从这三篇文章开始入手。
\begin{itemize}
\item[] [1998 ECCV] Active Appearance Models
\item[] [2001 PAMI] Active Appearance Models
\end{itemize}

\subsection{Active Shape Models}
\begin{itemize}
\item[] [1995 CVIU]Active Shape Models-Their Training and Application
\end{itemize}

\subsection{Background Modeling and Subtraction}
背景建模一直是视频分析尤其是目标检测中的一项关键技术。虽然最近一直有一些新技术的产生，demo效果也很好，比如基于dynamical Texture的方法。但最经典的还是Stauffer等在1999年和2000年提出的GMM方法，他们最大的贡献在于不用EM去做高斯拟合，而是采用了一种迭代的算法，这样就不需要保存很多帧的数据，节省了buffer。Zivkovic在2004年的ICPR和PAMI上提出了动态确定高斯数目的方法，把混合高斯模型做到了极致。这种方法效果也很好，而且易于实现。在OpenCV中有现成的函数可以调用。在背景建模大家族里，无参数方法（2000 ECCV）和Vibe方法也值得关注。
\begin{itemize}
\item[] [1997 PAMI] Pfinder Real-Time Tracking of the Human Body
\item[] [1999 CVPR] Adaptive Background Mixture Models for Real-time Tracking
\item[] [1999 ICCV] Wallflower Principles and Practice of Background Maintenance
\item[] [2000 ECCV] Non-parametric Model for Background Subtraction
\item[] [2000 PAMI] Learning Patterns of Activity Using Real-Time Tracking
\item[] [2002 PIEEE] Background and Foreground Modeling Using Nonparametric
kernel Density Estimation for Visual Surveillance
\item[] [2004 ICPR] Improved Adaptive Gaussian Mixture Model for Background Subtraction
\item[] [2004 PAMI] Recursive Unsupervised Learning of Finite Mixture Models
\item[] [2006 PRL] Efficient Adaptive Density Estimation Per Image Pixel for the Task of Background Subtraction
\item[] [2011 TIP] ViBe a Universal Background Subtraction Algorithm for Video Sequences
\end{itemize}

\subsection{Bag of Words}
词袋，在这方面暂时没有什么研究。列出三篇引用率很高的文章，以后逐步解剖之。
\begin{itemize}
\item[] [2003 ICCV] Video Google a Text Retrieval Approach To Object Matching in Videos
\item[] [2004 ECCV] Visual Categorization With Bags of Keypoints
\item[] [2006 CVPR] Beyond Bags of Features Spatial Pyramid Matching for Recognizing Natural Scene Categories
\end{itemize}

\subsection{BRIEF}
BRIEF是Binary Robust Independent Elementary Features的简称，是近年来比较受关注的特征描述的方法。ORB也是基于BRIEF的。
\begin{itemize}
\item[] [2010 ECCV] BRIEF Binary Robust Independent Elementary Features
\item[] [2011 ICCV] ORB an Efficient Alternative To SIFT Or SURF
\item[] [2012 PAMI] BRIEF Computing a Local Binary Descriptor Very Fast
\end{itemize}

\subsection{Camera Calibration and Stereo Vision}
非常不熟悉的领域。仅仅列出了十来篇重要的文献，供以后学习。
\begin{itemize}
\item[] [1979 Marr] A Computational Theory of Human Stereo Vision
\item[] [1985] Computational Vision and Regularization Theory
\item[] [1987 IEEE] A Versatile Camera Calibration Technique For
high-accuracy 3D Machine Vision Metrology Using Off-the-shelf TV Cameras and Lenses
\item[] [1987] Probabilistic Solution of Ill-Posed Problems in Computational Vision
\item[] [1988 PIEEE] Ill-Posed Problems in Early Vision
\item[] [1989 IJCV] Kalman Filter-based Algorithms for Estimating Depth From Image Sequences
\item[] [1990 IJCV] Relative Orientation
\item[] [1990 IJCV] Using Vanishing Points for Camera Calibration
\item[] [1992 ECCV] Camera Self-calibration Theory and Experiments
\item[] [1992 IJCV] A Theory of Self-calibration of a Moving Camera
\item[] [1992 PAMI] Camera Calibration With Distortion Models and Accuracy Evaluation
\item[] [1994 IJCV] The Fundamental Matrix Theory, Algorithms, and Stability Analysis
\item[] [1994 PAMI] A Stereo Matching Algorithm With an Adaptive Window Theory and Experiment
\item[] [1999 ICCV] Flexible Camera Calibration By Viewing a Plane From Unknown Orientations
\item[] [1999 IWAR] Marker Tracking and Hmd Calibration for a Video-based Augmented Reality Conferencing System
\item[] [2000 PAMI] A Flexible New Technique for Camera Calibration
\end{itemize}

\subsection{Color and Histogram Feature}
这里面主要来源于图像检索，早期的图像检测基本基于全局的特征，其中最显著的就是颜色特征。这一部分可以和前面的Color知识放在一起的。
\begin{itemize}
\item[] [1995 SPIE] Similarity of Color Images
\item[] [1996 PR] IMAGE RETRIEVAL USING COLOR AND SHAPE
\item[] [1996] Comparing Images Using Color Coherence Vectors
\item[] [1997 ] Image Indexing Using Color Correlograms
\item[] [2001 TIP] An Efficient Color Representation for Image Retrieval
\item[] [2009 CVIU] Performance Evaluation of Local Colour Invariants
\end{itemize}

\subsection{Deformable Part Model}
大红大热的DPM，在OpenCV中有一个专门的topic讲DPM和latent Svm
\begin{itemize}
\item[] [2008 CVPR] A Discriminatively Trained, Multiscale, Deformable Part Model
\item[] [2010 CVPR] Cascade Object Detection With Deformable Part Models
\item[] [2010 PAMI] Object Detection With Discriminatively Trained Part-Based Models
\end{itemize}

\subsection{Distance Transformations}
距离变换，在OpenCV中也有实现。用来在二值图像中寻找种子点非常方便。
\begin{itemize}
\item[] [1986 CVGIP] Distance Transformations in Digital Images
\item[] [2008 ACM] 2D Euclidean Distance Transform Algorithms a Comparative Survey
\end{itemize}

\subsection{Face Detection}
最成熟最有名的当属Haar+Adaboost
\begin{itemize}
\item[] [1998 PAMI] Neural Network-Based Face Detection
\item[] [2002 PAMI] Detecting Faces in Images a Survey
\item[] [2002 PAMI] Face Detection in Color Images
\item[] [2004 IJCV] Robust Real-Time Face Detection
\end{itemize}

\subsection{Face Recognition}
不熟悉，简单罗列之。
\begin{itemize}
\item[] [1991] Face Recognition Using Eigenfaces
\item[] [2000 PAMI] Automatic Analysis of Facial Expressions the State of the Art
\item[] [2000] Face Recognition a Literature Survey
\item[] [2006 PR] Face Recognition From a Single Image Per Person a Survey
\item[] [2009 PAMI] Robust Face Recognition Via Sparse Representation
\end{itemize}

\subsection{FAST}
用机器学习的方法来提取角点，号称很快很好。
\begin{itemize}
\item[] [2006 ECCV] Machine Learning for High-speed Corner Detection
\item[] [2010 PAMI] Faster and Better a Machine Learning Approach To Corner Detection
\end{itemize}

\subsection{Feature Extraction}
这里的特征主要都是各种不变性特征，SIFT，Harris，MSER等也属于这一类。把它们单独列出来是因为这些方法更流行一点。关于不变性特征，王永明与王贵锦合著的《图像局部不变性特征与描述》写的还不错。Mikolajczyk在2005年的PAMI上的文章以及2007年的综述是不错的学习材料。
\begin{itemize}
\item[] [1989 PAMI] On the Detection of Dominant Points on Digital Curves
\item[] [1997 IJCV] SUSAN—A New Approach To Low Level Image Processing
\item[] [2004 IJCV] Matching Widely Separated Views Based on Affine Invariant Regions
\item[] [2004 IJCV] Scale \& Affine Invariant Interest Point Detectors
\item[] [2005 PAMI] A Performance Evaluation of Local Descriptors
\item[] [2006 IJCV] A Comparison of Affine Region Detectors
\item[] [2007 FAT] Local Invariant Feature Detectors - a Survey
\item[] [2011 IJCV] Evaluation of Interest Point Detectors and Feature Descriptors
\end{itemize}

\subsection{Feature Matching}
Fua课题组在今年PAMI上的一篇文章，感觉还不错
\begin{itemize}
\item[] [2012 PAMI] LDAHash Improved Matching With Smaller Descriptors
\end{itemize}

\subsection{Harris}
虽然过去了很多年，Harris角点检测仍然广泛使用，而且基于它有很多变形。如果仔细看了这种方法，从直观也可以感觉到这是一种很稳健的方法。
\begin{itemize}
\item[] [1988 Harris] A Combined Corner and Edge Detector
\end{itemize}

\subsection{Histograms of Oriented Gradients}
HoG方法也在OpenCV中实现了：HOGDescriptor。
\begin{itemize}
\item[] [2005 CVPR] Histograms of Oriented Gradients for Human Detection
NavneetDalalThesis.pdf
\end{itemize}

\subsection{Image Distance}
\begin{itemize}
\item[] [1993 PAMI] Comparing Images Using the Hausdorff Distance
\end{itemize}

\subsection{Image Stitching}
图像拼接，另一个相关的词是Panoramic。在Computer Vision: Algorithms and Applications一书中，有专门一章是讨论这个问题。这里的两面文章一篇是综述，一篇是这方面很经典的文章。
\begin{itemize}
\item[] [2006 Fnd] Image Alignment and Stitching a Tutorial
\item[] [2007 IJCV] Automatic Panoramic Image Stitching Using Invariant Features
\end{itemize}

\subsection{KLT}
KLT跟踪算法，基于Lucas-Kanade提出的配准算法。除了三篇很经典的文章，最后一篇给出了OpenCV实现KLT的细节。
\begin{itemize}
\item[] [1981] An Iterative Image Registration Technique With an Application To Stereo Vision Full Version
\item[] [1994 CVPR] Good Features To Track
\item[] [2004 IJCV] Lucas-Kanade 20 Years on a Unifying Framework
Pyramidal Implementation of the Lucas Kanade Feature Tracker OpenCV
\end{itemize}

\subsection{Local Binary Pattern}
LBP。OpenCV的Cascade分类器也支持LBP，用来取代Haar特征。
\begin{itemize}
\item[] [2002 PAMI] Multiresolution Gray-scale and Rotation Invariant Texture Classification With Local Binary Patterns
\item[] [2004 ECCV] Face Recognition With Local Binary Patterns
\item[] [2006 PAMI] Face Description With Local Binary Patterns
\item[] [2011 TIP] Rotation-Invariant Image and Video Description With Local Binary Pattern Features
\end{itemize}

\subsection{Low-Level Vision}
关于Low Level Vision的两篇很不错的文章
\begin{itemize}
\item[] [1998 TIP] A General Framework for Low Level Vision
\item[] [2000 IJCV] Learning Low-Level Vision
\end{itemize}

\subsection{Mean Shift}
均值漂移算法，在跟踪中非常流行的方法。Comaniciu在这个方面做出了重要的贡献。最后三篇，一篇是CVIU上的top Download文章，一篇是最新的PAMI上关于Mean Shift的文章，一篇是OpenCV实现的文章。
\begin{itemize}
\item[] [1995 PAMI] Mean Shift, Mode Seeking, and Clustering
\item[] [2002 PAMI] Mean Shift a Robust Approach Toward Feature Space Analysis
\item[] [2003 CVPR] Mean-shift Blob Tracking Through Scale Space
\item[] [2009 CVIU] Object Tracking Using SIFT Features and Mean Shift
\item[] [2012 PAMI] Mean Shift Trackers With Cross-Bin Metrics
OpenCV Computer Vision Face Tracking for Use in a Perceptual User Interface
\end{itemize}

\subsection{MSER}
这篇文章发表在2002年的BMVC上，后来直接录用到2004年的IVC上，内容差不多。MSER在Sonka的书里面也有提到。
\begin{itemize}
\item[] [2002 BMVC] Robust Wide Baseline Stereo From Maximally Stable Extremal Regions
\item[] [2003] MSER Author Presentation
\item[] [2004 IVC] Robust Wide-baseline Stereo From Maximally Stable Extremal Regions
\item[] [2011 PAMI] Are MSER Features Really Interesting
\end{itemize}

\subsection{Object Detection}
首先要说的是第一篇文章的作者，Kah-Kay Sung。他是MIT的博士，后来到新加坡国立任教，极具潜力的一个老师。不幸的是，他和他的妻子都在2000年的新加坡空难中遇难，让人唏嘘不已。
\verb|http://en.wikipedia.org/wiki/Singapore_Airlines_Flight_006|
最后一篇文章也是Fua课题组的，作者给出的demo效果相当好。
\begin{itemize}
\item[] [1998 PAMI] Example-based Learning for View-based Human Face Detection
\item[] [2003 IJCV] Learning the Statistics of People in Images and Video
\item[] [2011 PAMI] Learning To Detect a Salient Object
\item[] [2012 PAMI] A Real-Time Deformable Detector
\end{itemize}

\subsection{Object Tracking}
跟踪也是计算机视觉中的经典问题。粒子滤波，卡尔曼滤波，KLT，mean Shift，光流都跟它有关系。这里列出的是传统意义上的跟踪，尤其值得一看的是2008的Survey和2003年的Kernel Based Tracking。
\begin{itemize}
\item[] [2003 PAMI] Kernel-based Object Tracking
\item[] [2007 PAMI] Tracking People By Learning Their Appearance
\item[] [2008 ACM] Object Tracking a Survey
\item[] [2008 PAMI] Segmentation and Tracking of Multiple Humans in Crowded Environments
\item[] [2011 PAMI] Hough Forests for Object Detection, Tracking, and Action Recognition
\item[] [2011 PAMI] Robust Object Tracking With Online Multiple Instance Learning
\item[] [2012 IJCV] PWP3D Real-Time Segmentation and Tracking of 3D Objects
\end{itemize}

\subsection{OCR}
一个非常成熟的领域，已经很好的商业化了。
\begin{itemize}
\item[] [1992 IEEE] Historical Review of OCR Research and Development
Video OCR a Survey and Practitioner's Guide
\end{itemize}

\subsection{Optical Flow}
光流法，视频分析所必需掌握的一种算法。
\begin{itemize}
\item[] [1981 AI] Determine Optical Flow
\item[] [1994 IJCV] Performance of Optical Flow Techniques
\item[] [1995 ACM] The Computation of Optical Flow
\item[] [2004 TR] Tutorial Computing 2D and 3D Optical Flow
\item[] [2005 BOOK] Optical Flow Estimation
\item[] [2008 ECCV] Learning Optical Flow
\item[] [2011 IJCV] A Database and Evaluation Methodology for Optical Flow
\end{itemize}

\subsection{Particle Filter}
粒子滤波，主要给出的是综述以及1998 IJCV上的关于粒子滤波发展早期的经典文章。
\begin{itemize}
\item[] [1998 IJCV] CONDENSATION—Conditional Density Propagation for Visual Tracking
\item[] [2002 TSP] A Tutorial on Particle Filters for Online Nonlinear Non-Gaussian Bayesian Tracking
\item[] [2002 TSP] Particle Filters for Positioning, Navigation, and Tracking
\item[] [2003 SPM] Particle Filter
\end{itemize}

\subsection{Pedestrian and Human Detection}
仍然是综述类，关于行人和人体的运动检测和动作识别。
\begin{itemize}
\item[] [1999 CVIU] Visual Analysis of Human Movement\_ a Survey
\item[] [2001 CVIU] A Survey of Computer Vision-Based Human Motion Capture
\item[] [2005 TIP] Image Change Detection Algorithms a Systematic Survey
\item[] [2006 CVIU] A Survey of Avdances in Vision Based Human Motion Capture
\item[] [2007 CVIU] Vision-based Human Motion Analysis an Overview
\item[] [2007 IJCV] Pedestrian Detection Via Periodic Motion Analysis
\item[] [2007 PR] A Survey of Skin-color Modeling and Detection Methods
\item[] [2010 IVC] A Survey on Vision-based Human Action Recognition
\item[] [2012 PAMI] Pedestrian Detection an Evaluation of the State of the Art
\end{itemize}

\subsection{Scene Classification}
当相机越来越傻瓜化的时候，自动场景识别就非常重要。这是比拼谁家的Auto功能做的比较好的时候了。
\begin{itemize}
\item[] [2001 IJCV] Modeling the Shape of the Scene a Holistic Representation of the Spatial Envelope
\item[] [2001 PAMI] Visual Word Ambiguity
\item[] [2007 PAMI] A Thousand Words in a Scene
\item[] [2010 PAMI] Evaluating Color Descriptors for Object and Scene Recognition
\item[] [2011 PAMI] CENTRIST a Visual Descriptor for Scene Categorization
\end{itemize}

\subsection{Shadow Detection}
\begin{itemize}
\item[] [2003 PAMI] Detecting Moving Shadows-- Algorithms and Evaluation
\end{itemize}

\subsection{Shape}
关于形状，主要是两个方面：形状的表示和形状的识别。形状的表示主要是从边缘或者区域当中提取不变性特征，用来做检索或者识别。这方面Sonka的书讲的比较系统。2008年的那篇综述在这方面也讲的不错。至于形状识别，最牛的当属J Malik等提出的Shape Context。
\begin{itemize}
\item[] [1993 PR] IMPROVED MOMENT INVARIANTS FOR SHAPE DISCRIMINATION
\item[] [1993 PR] Pattern Recognition By Affine Moment Invariants
\item[] [1996 PR] IMAGE RETRIEVAL USING COLOR AND SHAPE
\item[] [2001 SMI] Shape Matching Similarity Measures and Algorithms
\item[] [2002 PAMI] Shape Matching and Object Recognition Using Shape Contexts
\item[] [2004 PR] Review of Shape Representation and Description Techniques
\item[] [2006 PAMI] Integral Invariants for Shape Matching
\item[] [2008] A Survey of Shape Feature Extraction Techniques
\end{itemize}

\subsection{SIFT}
关于SIFT，实在不需要介绍太多，一万多次的引用已经说明问题了。SURF和PCA-SIFT也是属于这个系列。后面列出了几篇跟SIFT有关的问题。
\begin{itemize}
\item[] [1999 ICCV] Object Recognition From Local Scale-invariant Features
\item[] [2000 IJCV] Evaluation of Interest Point Detectors
\item[] [2003 CVIU] Speeded-Up Robust Features (SURF)
\item[] [2004 CVPR] PCA-SIFT a More Distinctive Representation for Local Image Descriptors
\item[] [2004 IJCV] Distinctive Image Features From Scale-Invariant Keypoints
\item[] [2010 IJCV] Improving Bag-of-Features for Large Scale Image Search
\item[] [2011 PAMI] SIFTflow Dense Correspondence Across Scenes and Its Applications
\end{itemize}

\subsection{SLAM}
Simultaneous Localization and Mapping, 同步定位与建图。
SLAM问题可以描述为: 机器人在未知环境中从一个未知位置开始移动,在移动过程中根据位置估计和地图进行自身定位,同时在自身定位的基础上建造增量式地图，实现机器人的自主定位和导航。
\begin{itemize}
\item[] [2002 PAMI] Simultaneous Localization and Map-Building Using Active Vision
\item[] [2007 PAMI] MonoSLAM Real-Time Single Camera SLAM
\end{itemize}

\subsection{Texture Feature}
纹理特征也是物体识别和检索的一个重要特征集。
\begin{itemize}
\item[] [1973] Textural Features for Image Classification
\item[] [1979 ] Statistical and Structural Approaches To Texture
\item[] [1996 PAMI] Texture Features for Browsing and Retrieval of Image Data
\item[] [2002 PR] Brief Review of Invariant Texture Analysis Methods
\item[] [2012 TIP] Color Local Texture Features for Color Face Recognition
\end{itemize}

\subsection{TLD}
Kadal创立了TLD，跟踪学习检测同步进行，达到稳健跟踪的目的。他的两个导师也是大名鼎鼎，一个是发明MSER的Matas，一个是Mikolajczyk。他还创立了一个公司TLD Vision S.r.o. 这里给出了他的系列文章，最后一篇是刚出来的PAMI。
\begin{itemize}
\item[] [2009] Online Learning of Robust Object Detectors During Unstable Tracking
\item[] [2010 CVPR] P-N Learning Bootstrapping Binary Classifiers By Structural Constraints
\item[] [2010 ICIP] FACE-TLD TRACKING-LEARNING-DETECTION APPLIED TO FACES
\item[] [2012 PAMI] Tracking-Learning-Detection
\end{itemize}

\subsection{Video Surveillance}
前两篇是两个很有名的视频监控系统，里面包含了很丰富的信息量，比如CMU的那个系统里面的背景建模算法也是相当简单有效的。最后一篇是比较近的综述。
\begin{itemize}
\item[] [2000 CMU TR] A System for Video Surveillance and Monitoring
\item[] [2000 PAMI] W4-- Real-time Surveillance of People and Their Activities
\item[] [2008 MVA] The Evolution of Video Surveillance an Overview
\end{itemize}

\subsection{Viola-Jones}
Haar+Adaboost的弱弱联手，组成了最强大的利器。在OpenCV里面有它的实现，也可以选择用LBP来代替Haar特征。
\begin{itemize}
\item[] [2001 CVPR] Rapid Object Detection Using a Boosted Cascade of Simple Features
\item[] [2004 IJCV] Robust Real-time Face Detection
\end{itemize}
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